Category: HR and Payroll

With the recent hulabaloo re: Bitcoin, Ethereum etc, I thought I’d spend some time getting myself familiar with these concepts and crypto-currencies.

As some-one who has owned a bit of gold for a long time (seen ups and downs) – due to my inherent distrust in governments being able to control their spending – some of the features of crypto-currencies appeal to me.

The benefits :

Cryptocurrencies (eg Bitcoin) can be designed to have a cap / limited amount ever to be issued. This means that they should actually be scarce – you know like the resources on this planet – and through that could be good ‘stores of value’ – basically as a currency should be.

As a medium of exchange – with the recent Segwit/Bitcoin cash fork, Bitcoin has a chance to become a good medium of exchange – both for micropayments (e.g via the Lightning network) or currency transfers.

Programmable blockchains like Ethereum allow for smart contracts to be implemented on the chain. The ‘ethereum computer’ is actually decentralized network of computers that is ‘Turing complete‘ – meaning that it’s pretty much up to you how complex code you want to write on it.

Due to the above programmable nature especially of Ethereum – I think we will see many use cases which will be tried – some will fail, some will succeed. If you are interested in how blockchain could be applied for example in HR BPO – please contact me here.

The pitfalls :

The main pitfall I would say is still that you have to do your own homework on who / what to trust, but I guess that applies in life generally… The other pitfalls still include a truly easy to use user interface / wallet. However I will investigate those more in detail next.

So yesterday I went to a meetup event called ‘Bitcoin and Cryptocurrency Intro How To Make Money Passively From Home’. The event was hosted in a local Panera Bread, with about 15 people attending. The pitch was for a ‘company’ called BitConnect where the premise is:

You buy their coin (bitconnect coin or BCC) using Bitcoin(BTC).

The BCC is converted back to USD – and using their HFT (high-frequency trading) algorithms they trade USD vs BTC.

Somehow the Bitconnect team are supposedly able to make daily profits (‘interest’) according the presenter/this chart (no down days):

They state that the high market cap (around 786M USD today, August 18th 2017) is proof of the legitimacy of this platform.

OK, call me a sceptic, here’s why:

When I asked about the team behind this, the presenter dodged the question, and no information is available that I can find on the bitconnect.co website. No developers who want to have their names publicly associated with it? Hmm. No early investors? Hmm.

From the site “Build trust and reputation in bitcoin and cryptocurrency ecosystem with Open-source platform”. There is no information about what is open source, and this code on Github has one contributor..

As Steemit writes – it’s too good to be true, no down days, guaranteed returns, referral schemes etc..

You are supposed to send BTC to them – but you are ‘locked in’ for up to 299 days. Guess what will happen to your BTC if they run out of new patsies to pay the old ones..

Bottom-line – there is a ton value that can be created on blockchains – but people – pls do your homework. And if you / or some-one you know is considering bitconnect – caveat emptor…

These days we seem to be bombarded daily with articles about Artificial Intelligence, Machine Learning and some version of ‘robots will take your job’… However as of 2016, machine learning is a buzzword, and according to the Gartner hype cycle of 2016, at its peak of inflated expectations.[12] This is because finding patterns is hard, often we don’t have enough training data available, and also because of the high expectations it often fails to deliver – so hold your expectations of having a ‘Jarvis‘-like presence guiding you along – we’re not quite there yet 🙂

With that in mind, I thought it would be interesting as an HR/IT professional to try to identify possible use cases in HR for machine learning, and then identify where the most value can be gained from using it. And by HR /Human Resources here I mean all the ‘branches’ of it – including Recruitment, Personnel Administration, Talent & Performance Management, Payroll, Time management, Benefits, Compensation etc etc. I’d be happy to have comments & additions be added to the list, so leave your suggestion/s in the comments section below. With that said, here we go:

Use cases for Machine Learning

1/ Recruitment, candidate attraction – explore your social media data / mentions, and dig down where traffic – with a focus on target market (say front-end engineers) are coming from, in order to achieve more predictable candidate flow. And remember positive impression matter a lot, as Chobani has shown with HR as PR. Then use blog post writing software for example using Narrative Language Generation, in order to improve social media presence.

2/ Recruitment to Talent Management – explore your data to classify from which industries / areas successful candidates have come. Can you find commonalities between solid performers? You can use for example K-means clustering to find the commonalities between the good performers. However as this article explains, this can lead to bias in your selection (say as most CS graduates are male), so it’s important to back test your algorithm to avoid issues down the road.

4/ HR customer service – as explored here, with Natural Language Understanding getting better and better it’s possible to create chatbots (or conversational agents) that help answer employee questions, route questions to human agents, perhaps even do simple updates. Companies in this space include eg. Talla, Kylie,ai. Custom implementations could be done with Slack, Microsoft’s botframework, or IBM Watson conversations etc. I’m working on a ‘Benefits’ chatbot using Watson Conversations and SAP Hana Cloud Platform, welcome to ping me here if you are interested in this space.

5/ New hire on-boarding – chatbots these days use NLP (Natural Language Processing) which ‘understands’ what the employee is intending to communicate. You could create a chatbot or ‘conversational agent’ with new hire guidance, FAQs, role documentation.

6/ Retaining valuable employees – Use salary data, performance, organizational data etc to explore correlations, and ultimately predict which factors predispose to voluntary terminations. You could do these predictions eg. with Ensemble methods / Random Forests, or use classification tools like support vector machines or a K-neighbors classifier to group the employees into ‘buckets’ of ‘high / medium / low’ chance of leaving.

8/Payroll & Time Management employee questions – provide a chatbot to answer employee queries re: payslips, overtime, absence policies etc, as explored here. The bot also handle repetitive tasks which can be demoralising and unfulfilling (e.g. pre-qualification (what’s this issue about?) or authorisation (how can you prove you are John Smith).

9/ Payroll data connection to talent management – highly structured data found in conventional databases tend to support traditional, highly analytical machine learning approaches. If you have 10+ years of transactional data, then you could use machine learning to find correlations between employee characteristics and pay raises for example.

10/ Career / role recommender system – If you have a large enough organisation with different departments, very varied roles and the data to track where people have previously transferred to or changed roles into, then you could also create a recommender system based on interests, skills, performance, where suitable new roles in your organization could be found. Here’s a comprehensive list of different recommender system – eg. with tools from Amazon, Microsoft Azure or IBM Watson.

Hope this whet your appetite a bit to explore Machine Learning for HR a bit further – and let me any comments or additions below!

This blog page is an experiment with creating a chatbot -or ‘conversational agent’ which can help an HR/Payroll organisation customer service agents to handle the routine, easier questions.

Service center agents often have to handle repetitive tasks which can be demoralising and unfulfilling (e.g. pre-qualification (what’s this issue about?) or authorisation (how can you prove you are John Smith)). The chatbot can do that instead.

Customers don’t like being put on hold or wait in a phone queue – the chatbot is always there and ready to answer questions. Finally customers don’t like to repeat themselves- since all the conversations are logged in the thread, the information is available also to a human customer service rep if required.

I’ve built this below CA (conversational agent) to be able handle simple queries like:

“can you send me my payslip”?

“What was my overtime balance for the previous month”?

“Connect with support team”

etc…

There is an “authorisation step” when you are requesting either a payslip or overtime, accepted name values are: “John”, “Sally” and “Pentti”.

These are just some simple examples, more can easily be configured. Give it a go here:

As you’ll note there is no back-end integration at the moment, however the conversational agent is able to greet the customer, understand the queries, do an authorisation check and direct her further if needed.

Pls let me know what you think in the comments, or contact me via the contact page.